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Duration 14 hours
Course Outline
Foundations of Agent Builder and RAG
- Core capabilities of Agent Builder
- RAG essentials and applicable scenarios
- Real-world use cases and success stories
Environment Setup
- Configuring your Vertex AI workspace
- Linking search and vector storage solutions
- Practical lab: Preparing the development environment
Constructing Grounded Agent Workflows
- Establishing agent objectives and dialogue structures
- Aligning data sources with retrieval strategies
- Practical lab: Developing a conversation flow
Building RAG Pipelines
- Processing documents and generating embeddings
- Applying retriever and re-ranker patterns
- Practical lab: Assembling a RAG pipeline
Enterprise Integrations and Data Management
- Establishing secure connections to internal systems
- Implementing data governance and access controls
- Practical lab: Connecting enterprise data sources
Testing, Evaluation, and Refinement
- Conducting prompt tests and analyzing metrics
- Simulating user interactions and validation methods
- Practical lab: Evaluating and tuning the agent
Deployment, Oversight, and Maintenance
- Exploring deployment strategies and scalability
- Tracking performance, relevance, and data drift
- Creating operational playbooks for updates and rollbacks
Recap and Future Directions
Requirements
- Foundational understanding of natural language processing
- Practical experience with cloud services and API integration
- Working familiarity with search engines and vector databases
Target Audience
- Software Developers
- Solution Architects
- Product Managers
